How to work with Parallel Data in Apache Spark using SQL

Apache Spark is a fast and general engine for large-scale data processing. When paired with the CData JDBC Driver for Parallel, Spark can work with live Parallel data. This article describes how to connect to and query Parallel data from a Spark shell.

The CData JDBC Driver offers unmatched performance for interacting with live Parallel data due to optimized data processing built into the driver. When you issue complex SQL queries to Parallel, the driver pushes supported SQL operations, like filters and aggregations, directly to Parallel and utilizes the embedded SQL engine to process unsupported operations (often SQL functions and JOIN operations) client-side. With built-in dynamic metadata querying, you can work with and analyze Parallel data using native data types.

Install the CData JDBC Driver for Parallel

Download the CData JDBC Driver for Parallel installer, unzip the package, and run the JAR file to install the driver.

Start a Spark Shell and Connect to Parallel Data

1. Open a terminal and start the Spark shell with the CData JDBC Driver for Parallel JAR file as the jars parameter:

   $ spark-shell --jars /CData/CData JDBC Driver for Parallel/lib/cdata.jdbc.api.jar
   

2. With the shell running, you can connect to Parallel with a JDBC URL and use the SQL Context load() function to read a table.

   The Parallel API uses API Key authentication via the x-api-key request header.

   Using API Key Authentication

   Your Parallel API key is required to create a connection. To obtain your API key:

   1. Log into your Parallel account at app.parallel.ai.
   2. Navigate to Settings or API Keys in your account dashboard.
   3. Generate or copy your API key.

   After obtaining your API key, set the following connection properties:

   • AuthScheme: Set this to APIKey.
   • APIKey: Set this to your Parallel API key.

   Example connection string:

   Profile=C:\profiles\Parallel.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_api_key';
   

   Built-in Connection String Designer

   For assistance in constructing the JDBC URL, use the connection string designer built into the Parallel JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

   java -jar cdata.jdbc.api.jar
   

   Fill in the connection properties and copy the connection string to the clipboard.

   👁 Using the built-in connection string designer to generate a JDBC URL (Salesforce is shown.)
   

   Configure the connection to Parallel, using the connection string generated above.

   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=C:\profiles\Parallel.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_api_key';").option("dbtable","MonitorEvents").option("driver","cdata.jdbc.api.APIDriver").load()
   

3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the Parallel data as a temporary table:

   scala> api_df.registerTable("monitorevents")

5. Perform custom SQL queries against the Data using commands like the one below:

   scala> api_df.sqlContext.sql("SELECT , FROM MonitorEvents WHERE MonitorId = mon_abc123").collect.foreach(println)

   You will see the results displayed in the console, similar to the following:

   👁 Data in Apache Spark (Salesforce is shown)
   

Using the CData JDBC Driver for Parallel in Apache Spark, you are able to perform fast and complex analytics on Parallel data, combining the power and utility of Spark with your data. Download a free, 30 day trial of any of the hundreds of CData JDBC Drivers and get started today.